非单态模糊神经网络的复杂性降低

Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569) Pub Date : 2001-07-25 DOI:10.1109/NAFIPS.2001.943619

A. Várkonyi-Kóczy, K. Lei, Masashi Sugiyama, H. Asai

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引用次数: 0

摘要

提出了一种基于奇异值分解的模糊神经网络约简技术。在模糊理论中，还采用了基于非单例结果的Takagi-Sugeno模型。将非单点模糊模型应用于模糊神经网络，得到了非单点模糊神经网络。本工作的主要目的是将基于奇异值分解的模糊神经网络约简技术扩展到非单态神经网络。

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Complexity reduction to non-singleton fuzzy-neural network

A singular value decomposition (SVD) based reduction technique has been proposed for a singleton-based fuzzy neural network. In fuzzy theory, the use of the non-singleton consequent-based Takagi-Sugeno model is also adopted. By applying a non-singleton-based fuzzy model to fuzzy neural networks, a non-singleton-based network is obtained. The main objective of this work is to extend the SVD-based reduction technique that has been proposed for fuzzy neural networks to non-singleton-based networks.

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Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)

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